Barium cycling in the Gulf of Aqaba
Bibliographic record
Abstract
The isotopic composition of barium (δ138Ba) has emerged as a powerful tracer of deep-ocean circulation, water mass provenance, and the oceanic Ba cycle. Although the δ138Ba of water masses is primarily controlled by the balance between pelagic barite precipitation and Ba resupply from ocean circulation, questions remain regarding the isotopic offset associated with pelagic barite formation and how the resultant Ba isotope compositions are transmitted through the water column to marine sediments. To address these questions, we conducted a time series study of dissolved, particulate, and sedimentary Ba chemistry in the Gulf of Aqaba (GOA), in the northern Red Sea, from January 2015 to April 2016. These data span significant seasonal changes in hydrography, primary productivity, and aerosol deposition, revealing three principal findings. First, the dissolved Ba chemistry of the GOA is vertically uniform across the time series, largely reflecting water mass advection from the Red Sea, with mean dissolved Ba concentrations of 47.9 ± 4.7 nmol kg−1 and mean δ138Ba = +0.55‰ ± 0.07‰ (±2 SD, n = 18). Second, despite significant variations in particulate matter composition and flux, the δ138Ba of sinking particulate Ba maintained a consistent isotope composition across different depths and over time at +0.09‰ ± 0.06‰ (n = 26). Consequently, these data imply a consistent Ba isotope offset of −0.46‰ ± 0.10‰ (±2 SD) between sinking particulates and seawater. This offset is similar to those determined in previous studies and indicates that it applies to particulates formed across diverse environmental conditions. Third, barite-containing sediment samples deposited in the GOA exhibit δ138Ba = +0.34‰ ± 0.03‰, which is offset by approximately +0.2‰ relative to sinking particles. While the specific mechanism driving this offset remains unresolved, our results highlight the importance of performing site-specific proxy validations and exercising careful site selection when applying novel paleoceanographic proxies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".